fit_statsmodels()

Fit a statsmodels model with output that is compatible with pymarginaleffects.

This function streamlines the process of fitting statsmodels models by: 1. Parsing the formula 2. Handling missing values 3. Creating model matrices 4. Fitting the model with specified options

Parameters

formula: (str) Model formula

• Example: “outcome ~ distance + incentive”

data: (pandas.DataFrame) Dataframe with the response variable and predictors.

engine: (callable) statsmodels model class (e.g., OLS, Logit)

kwargs_engine: (dict, default={}) Additional arguments passed to the model initialization.

• Example: {'weights': weights_array}

kwargs_fit: (dict, default={}) Additional arguments passed to the model’s fit method.

• Example: {'cov_type': 'HC3'}

Returns

(ModelStatsmodels) A fitted model wrapped in the ModelStatsmodels class for compatibility with marginaleffects.

Examples

from marginaleffects import fit_statsmodels, get_dataset, predictions, slopes, comparisons

import statsmodels.api as sm

data = get_dataset("thornton")

# Model with robust standard errors {.unnumbered}
model_robust = fit_statsmodels(
    formula="outcome ~ distance + incentive",
    data=data,
    engine=sm.OLS,
    kwargs_fit={"cov_type": "HC3"}
)

predictions(model_robust)
slopes(model_robust)
comparisons(model_robust)

Notes

The fitted model includes additional attributes:

• data: The processed data after listwise deletion
• formula: The original formula string
• formula_engine: Set to “statsmodels”
• model: The fitted statsmodels model object